Related papers: Machine Learning Assisted Insight to Spin Ice Dy$_…
Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those…
Dy2Ti2O7 has been advanced as an ideal spin ice. We present a neutron scattering investigation of a sample of 162Dy2Ti2O7. The scattering intensity has been mapped in zero applied field in the hhl and hk0 planes at temperatures between 0.05…
What is the correct low-energy spin Hamiltonian description of $\alpha$-RuCl$_3$? The material is a promising Kitaev spin liquid candidate, but is also known to order magnetically, the description of which necessitates additional…
Neutron diffraction has been used to investigate the magnetic correlations in single crystals of the spin ice materials Ho2Ti2O7 and Dy2Ti2O7 in an external magnetic field applied along either the [001] or [1-10] crystallographic…
We present a parametric study of the diffuse magnetic scattering at (1/2,1/2,1/2) positions in reciprocal space, ascribed to a frozen antiferromagnetic spin ice state in single crystalline Tb2Ti2O7. Our high-resolution neutron scattering…
The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods,…
The spin ice compound Dy$_2$Ti$_2$O$_7$ is well-known to realise a three-dimensional Coulomb spin liquid with magnetically charged monopole excitations. Its fate at low temperatures, however, remains an intriguing open question. Based on a…
We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t-distributed stochastic neighboring ensemble (t-SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary)…
Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from sensors operating under normal conditions and using the model…
A "supercooled" liquid develops when a fluid does not crystallize upon cooling below its ordering temperature. Instead, the microscopic relaxation times diverge so rapidly that, upon further cooling, equilibration eventually becomes…
The spin glass behavior of Y2Mo2O7 has puzzled physicists for nearly three decades. Free of bulk disorder within the resolution of powder diffraction methods, it is thought that this material is a rare realization of a spin glass resulting…
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For…
We investigated the nature of the freezing in the geometrically frustrated Heisenberg spin-glass Y2Mo2O7 by measuring the temperature dependence of the static internal magnetic field distribution above the spin-glass temperature, Tg, using…
We identify the low energy effective Hamiltonian that is expected to describe the low temperature properties of the frustrated magnet $\mathrm{Ca}_{10}\mathrm{Cr}_{7}\mathrm{O}_{28}$. Motivated by the fact that this effective Hamiltonian…
We present a methodology for generating Ising Hamiltonians of tunable complexity and with a priori known ground states based on a decomposition of the model graph into edge-disjoint subgraphs. The idea is illustrated with a spin-glass model…
Metallic spin glass systems, such as dilute magnetic alloys, are characterized by randomly distributed local moments coupled to each other through a long-range electron-mediated effective interaction. We present a scalable machine learning…
The spin ice materials Ho$_{2}$Ti$_{2}$O$_{7}$ and Dy$_{2}$Ti$_{2}$O$_{7}$ are by now perhaps the best-studied classical frustrated magnets. A crucial step towards the understanding of their low temperature behaviour -- both regarding their…
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set…
A dimer mean-field model for the Ising spin-glass is presented. Despite its simplicity it captures some of the essential features of the spin-glass physics. The distribution of the single-spin magnetization is determined from a…
A numerical model of interacting nanomagnetic elements is used to demonstrate active inference with a three dimensional Artificial Spin Ice structure. It is shown that thermal fluctuations can drive this magnetic spin system to evolve under…